Measuring the nursing workload per shift in the ICU

Purpose

In the intensive care unit (ICU) different strategies and workload measurement tools exist to indicate the number of nurses needed. The gathered information is always focused on manpower needed per 24?h. However, a day consists of several shifts, which may be unequal in nursing workload. The aim of this study was to evaluate if differences in nursing workload between consecutive shifts can be identified by a nursing workload measurement tool.

Methods

The nursing activities score (NAS) was registered per patient for every shift during a 4-week period in a prospective, observational research project in the surgical-pediatric ICU (SICU-PICU) and medical ICU (MICU) of an academic hospital.

Results

The NAS was influenced by the patient characteristics and the type of shift. Furthermore, the scores were lower during night shifts, in weekends and in MICU patients. Overall, the mean NAS per nurse per shift was 85.5?%, and the NAS per 24?h was 54.7?%.

Conclusion

This study has shown that the nursing workload can be measured per working shift. In the ICU, the NAS differentiates the nursing workload between shifts, patients and units.     

ACCCN Workforce Standards for Intensive Care Nursing: Systematic and evidence review,development, and appraisal

Background

The intensive care nursing workforce plays an essential role in the achievement of positive healthcare outcomes. A growing body of evidence indicates that inadequate nurse staffing and poor skill mix are associated with negative outcomes for patients, and potentially compromises nurses’ ability to maintain the safety of those in their care. In Australia, the Australian College of Critical Care Nurses (ACCCN) has previously published a position statement on intensive care staffing. There was a need for a stronger more evidence based document to support the intensive nursing workforce.

The characterisation of workloads and nursing staff allocation in intensive care units: A descriptive study using the Nursing Activities Score for the first time in Norway

Objective

This study compares the Nine Equivalents of Nursing Manpower Use Score (NEMS) to the Nursing Activities Score (NAS) in terms of characterising the nursing workload by examining and calculating the per-nurse NAS% over a 24-h period.

Method

The sample consisted of 235 patients from four volunteered for the study multidisciplinary ICUs in Norway. The daily NEMS, NAS and number of nurses who were involved in patient care per ICU were measured over one month from 2008 to 2009.

Results

The average length of stay for the included patients was 5 days, and the mean patient age was 52.8 years. The mean NEMS was 32.7 points (S.D., 8.98 points), and the mean NAS was 96.24% (S.D., 22.35%). Several nurses exhibited mean NEMS points that ranged from 16 to 39.7 per ICU per day. The correlation between the NEMS and NAS could only be separately determined for each ICU. The correlation was r = 0.16–0.40 [significant at the 0.01 level (2-tailed)] per unit. Depending on which unit was investigated, each nurse was observed to perform of capacity with a NAS as high as 75–90%.

Conclusion

The study suggests that the actual numbers of nurses might explain the calculated NAS of 75–90% per nurse.     

The relationship between inpatient cardiac surgery mortality and nurse numbers and educational level: Analysis of administrative data

Background

In most multicenter studies that examine the relationship between nurse staffing and patient safety, nurse-staffing levels are measured per hospital. This can obscure relationships between staffing and outcomes at the unit level and lead to invalid inferences.

Objective

In the present study, we examined the association between nurse-staffing levels in nursing units that treat postoperative cardiac surgery patients and the in-hospital mortality of these patients.

Design-setting-participants

We illustrated our approach by using administrative databases (Year 2003) representing all Belgian cardiac centers (n = 28), which included data from 58 intensive care and 75 general nursing units and 9054 patients.

Methods

We used multilevel logistic regression models and controlled for differences in patient characteristics, nursing care intensity, and cardiac procedural volume.

Results

Increased nurse staffing in postoperative general nursing units was significantly associated with decreased mortality. Nurse staffing in postoperative intensive care units was not significantly associated with in-hospital mortality possibly due to lack of variation in ICU staffing across hospitals.

Conclusion

This study, together with the international body of evidence, suggests that nurse staffing is one of several variables influencing patient safety. These findings further suggest the need to study the impact of nurse-staffing levels on in-hospital mortality using nursing-unit-level specific data.     

护理活动评分系统在ICU人力资源管理中的应用

目的探讨护理活动评分系统在ICU人力资源管理中的应用效果。方法采用护理活动评估量表（nursing activities scale,NAS）测量在本院ICU住院的149例病人的护理工作量,根据工作量计算护士人数,并预见性地合理安排每班护士人数。结果 ICU病人当天需要的护理工作量得分最低为30.50分,最高为129.70分,平均（53.23±18.12）分。所需护患比为1～2∶1的占了68.73%。实施护理活动评分系统后护理不良事件总发生率为18.12%,明显少于实施前33.12%,实施前后比较,χ2=8.909,P〈0.001,差异具有统计学意义。结论 ICU护士人力应以所收治病人实际需要的护理工作量进行动态合理分配。采用护理活动评分系统进行人力资源管理可提高ICU护理质量。     

Nursing Activities Score in the intensive care unit: analysis of the related factors.

BACKGROUND: High costs of intensive care as well as quality of care and patient safety demand measurement of nursing workload in order to determine nursing staff requirements. It is also important to be aware of the factors related to high patient care demands in order to help forecast staff requirements in intensive care units (ICUs). OBJECTIVES: To describe nursing workload using the Nursing Activities Score (NAS); to explore the association between NAS and patients variables, i.e. gender, age, length of stay (LOS), ICU discharge, treatment in the ICU, Simplified Acute Physiology Score II (SAPS II) and Therapeutic Interventions Scoring System-28 (TISS-28). METHODS: NAS, demographic data, SAPS II and TISS-28 were analysed among 200 patients from four different ICUs in a private hospital in S?o Paulo, Brazil. RESULTS: NAS median were 66.4%. High NAS scores (> 66.4%) were associated with death (p-value 0.006) and LOS (p-value 0.015). Logistic regression analysis demonstrated that TISS-28 scores above 23 and SAPS II scores above 46.5 points, classified as high, increased 5.45 and 2.78 times, respectively, the possibility of a high workload as compared to lower values of the same indexes. CONCLUSION: This study shows that the highest NAS scores were associated with increased mortality, LOS, severity of the patient illness (SAPS II), and particularly to TISS-28 in the ICU.     

Bed-to-nurse ratios, provision of basic nursing care, and in-hospital and 30-day mortality among acute stroke patients admitted to an intensive care unit: Cross-sectional analysis of survey and administrative data

Background

The literature reports inconsistent evidence of the effects of nurse staffing on mortality despite continuing examination of this association.

Objective

To examine differences in provision of basic nursing care and in-hospital and 30-day mortality by nurse staffing of ICUs and general wards among acute stroke patients admitted to ICUs during hospitalization.

Design

A cross-sectional design that included survey and administrative data.

Settings and participants

The study included 6957 patients with hemorrhagic and ischemic stroke who were admitted to ICUs of 185 Korean hospitals.

Methods

Nurse staffing of ICUs and general wards was graded based on the bed-to-nurse ratios of each hospital. Provision of basic care was measured by whether five activities, such as bathing and feeding assistance, were fully provided by ICU nursing staff without delegation to patient families. Hospitals were categorized into low, middle, and high mortality groups for in-hospital and 30-day mortality based on z-scores that indicated standardized difference between observed and expected mortality after controlling for patient characteristics.

Results

In 83.8% of hospitals, basic care was provided fully by ICU nursing staff. The overall in-hospital and 30-day mortality rates were 21.9 and 25.4%, respectively. Hospitals with higher ICU staffing were more likely to fully provide basic care. Better ICU and general staffing tended to be associated with lower in-hospital and 30-day mortality. Compared with in-hospital mortality, 30-day mortality had a more distinct increase as nurse staffing became worse.

Conclusion

The findings provide evidence that nurse staffing may impact provision of basic care and patient mortality and suggest the need for policies for providing adequate nurse staffing.     

The association between nurse staffing and omissions in nursing care: A systematic review

Aims

To identify nursing care most frequently missed in acute adult inpatient wards and to determine evidence for the association of missed care with nurse staffing.

Background

Research has established associations between nurse staffing levels and adverse patient outcomes including in‐hospital mortality. However, the causal nature of this relationship is uncertain and omissions of nursing care (referred as missed care, care left undone or rationed care) have been proposed as a factor which may provide a more direct indicator of nurse staffing adequacy.

Design

Systematic review.

Data Sources

We searched the Cochrane Library, CINAHL, Embase and Medline for quantitative studies of associations between staffing and missed care. We searched key journals, personal libraries and reference lists of articles.

Review Methods

Two reviewers independently selected studies. Quality appraisal was based on the National Institute for Health and Care Excellence quality appraisal checklist for studies reporting correlations and associations. Data were abstracted on study design, missed care prevalence and measures of association. Synthesis was narrative.

Results

Eighteen studies gave subjective reports of missed care. Seventy‐five per cent or more nurses reported omitting some care. Fourteen studies found low nurse staffing levels were significantly associated with higher reports of missed care. There was little evidence that adding support workers to the team reduced missed care.

Conclusions

Low Registered Nurse staffing is associated with reports of missed nursing care in hospitals. Missed care is a promising indicator of nurse staffing adequacy. The extent to which the relationships observed represent actual failures, is yet to be investigated.     

Nurse staffing and patient outcomes in Belgian acute hospitals: cross-sectional analysis of administrative data

Background

Studies have linked nurse staffing levels (number and skill mix) to several nurse-sensitive patient outcomes. However, evidence from European countries has been limited.

Objectives

This study examines the association between nurse staffing levels (i.e. acuity-adjusted Nursing Hours per Patient Day, the proportion of registered nurses with a Bachelor's degree) and 10 different patient outcomes potentially sensitive to nursing care.

Design-setting-participants

Cross-sectional analyses of linked data from the Belgian Nursing Minimum Dataset (general acute care and intensive care nursing units: n = 1403) and Belgian Hospital Discharge Dataset (general, orthopedic and vascular surgery patients: n = 260,923) of the year 2003 from all acute hospitals (n = 115).

Methods

Logistic regression analyses, estimated by using a Generalized Estimation Equation Model, were used to study the association between nurse staffing and patient outcomes.

Results

The mean acuity-adjusted Nursing Hours per Patient Day in Belgian hospitals was 2.62 (S.D. = 0.29). The variability in patient outcome rates between hospitals is considerable. The inter-quartile ranges for the 10 patient outcomes go from 0.35 for Deep Venous Thrombosis to 3.77 for failure-to-rescue. No significant association was found between the acuity-adjusted Nursing Hours per Patient Day, proportion of registered nurses with a Bachelor's degree and the selected patient outcomes.

Conclusion

The absence of associations between hospital-level nurse staffing measures and patient outcomes should not be inferred as implying that nurse staffing does not have an impact on patient outcomes in Belgian hospitals. To better understand the dynamics of the nurse staffing and patient outcomes relationship in acute hospitals, further analyses (i.e. nursing unit level analyses) of these and other outcomes are recommended, in addition to inclusion of other study variables, including data about nursing practice environments in hospitals.     

Benchmarking nurse staffing levels: the development of a nationwide feedback tool

Title. Benchmarking nurse staffing levels: the development of a nationwide feedback tool. Aim. This paper is a report of a study to develop a methodology that corrects nurse staffing for nursing care intensity in a way that allows nationwide benchmarking of nurse staffing data. Background. Although nurse workload measurement systems are recognized to be informative in nurse staffing decisions, they are rarely used. When these systems are used, however, it is only possible to compare units within hospitals, because currently available instruments are not standardized for comparisons beyond hospital boundaries. The Belgian Nursing Minimum Dataset (B‐NMDS) contains uniformly measured data about the intensity of nursing care and nurse staffing levels for all hospitals in Belgium. Method. We conducted a retrospective multilevel analysis of the B‐NMDS for the year 2003. The sample included 690,258 inpatient days for 298,691 patients, recorded from 1637 acute care nursing units in 115 hospitals. We corrected the number of nursing staff by using different covariates available in the B‐NMDS: intensity of nursing care, type of day (week vs. weekend), service type (general vs. intensive) and hospital type (academic vs. general). Findings. The multilevel approach allowed us to explain about 70% of the variability in the number of nursing staff per nursing unit using hospital type (P = 0·0053); intensity of nursing care (P < 0·0001) and service type (P < 0·0001) as the only covariates. Conclusion. The feedback tool we developed can inform nurse managers and policymakers about nursing intensity‐adjusted nurse staffing levels according to different benchmarks. Our study demonstrates that investing in large nursing datasets is appropriate for the international nursing community.     

Nurse staffing,medical staffing and mortality in Intensive Care: An observational study

Objectives

To investigate whether the size of the workforce (nurses, doctors and support staff) has an impact on the survival chances of critically ill patients both in the intensive care unit (ICU) and in the hospital.

Background

Investigations of intensive care outcomes suggest that some of the variation in patient survival rates might be related to staffing levels and workload, but the evidence is still equivocal.

Data

Information about patients, including the outcome of care (whether the patient lived or died) came from the Intensive Care National Audit & Research Centre (ICNARC) Case Mix Programme. An Audit Commission survey of ICUs conducted in 1998 gave information about staffing levels. The merged dataset had information on 65 ICUs and 38,168 patients. This is currently the best available dataset for testing the relationship between staffing and outcomes in UK ICUs.

Design

A cross-sectional, retrospective, risk adjusted observational study.

Methods

Multivariable, multilevel logistic regression.

Outcome Measures

ICU and in-hospital mortality.

Results

After controlling for patient characteristics and workload we found that higher numbers of nurses per bed (odds ratio: 0.90, 95% confidence interval: [0.83, 0.97]) and higher numbers of consultants (0.85, [0.76, 0.95]) were associated with higher survival rates. Further exploration revealed that the number of nurses had the greatest impact on patients at high risk of death (0.98, [0.96, 0.99]) whereas the effect of medical staffing was unchanged across the range of patient acuity (1.00, [0.97, 1.03]). No relationship between patient outcomes and the number of support staff (administrative, clerical, technical and scientific staff) was found. Distinguishing between direct care and supernumerary nurses and restricting the analysis to patients who had been in the unit for more than 8 h made little difference to the results. Separate analysis of in-unit and in-hospital survival showed that the clinical workforce in intensive care had a greater impact on ICU mortality than on hospital mortality which gives the study additional credibility.

Conclusion

This study supports claims that the availability of medical and nursing staff is associated with the survival of critically ill patients and suggests that future studies should focus on the resources of the health care team. The results emphasise the urgent need for a prospective study of staffing levels and the organisation of care in ICUs.     

治疗干预评分系统-28在ICU护理工作量评估中的应用研究

[目的]探讨治疗干预评分系统-28(TISS-28)在重症监护病房(ICU)护理工作量评估中的应用。[方法]应用TISS-28评分软件,选择ICU病人24h内病情最重时进行评分,并记录ICU每天护士岗位职数。[结果]每位病人每天TISS-28评分为29.11分±8.29分,ICU每天总的TISS-28评分为236.38分±73.75分,每天满足病人直接护理需要的护士岗位职数为17.87人±5.93人,而实际每天护士岗位职数为13.45人±2.70人;ICU护士每天人均TISS-28评分为52.84分±14.00分;1周中每天的总工作量比较差异无统计学意义(P>0.05),1周中每天护士人均护理工作量比较差异有统计学意义(P<0.05)。[结论]TISS-28可准确、简便地测量ICU护理工作量,ICU护士处于超负荷工作状态,ICU护士人力资源现状与所需存在一定的差距。     

治疗干预评分系统-28在ICU护理工作量评估中的应用研究

[目的]探讨治疗干预评分系统-28（TISS-28）在重症监护病房（ICU）护理工作量评估中的应用。[方法]应用TISS-28评分软件,选择ICU病人24h内病情最重时进行评分,并记录ICU每天护士岗位职数。[结果]每位病人每天TISS-28评分为29.11分±8.29分,ICU每天总的TISS-28评分为236.38分±73.75分,每天满足病人直接护理需要的护士岗位职数为17.87人±5.93人,而实际每天护士岗位职数为13.45人±2.70人;ICU护士每天人均TISS-28评分为52.84分±14.00分;1周中每天的总工作量比较差异无统计学意义（P〉0.05）,1周中每天护士人均护理工作量比较差异有统计学意义（P〈0.05）。[结论]TISS-28可准确、简便地测量ICU护理工作量,ICU护士处于超负荷工作状态,ICU护士人力资源现状与所需存在一定的差距。     

Nursing activities score (NAS): A proposal for practical application in intensive care units

For over 30 years in an attempt to demonstrate the cost-benefit ratio of the intensive care unit (ICU) a variety of tools have been developed to measure not only the severity of illness of the patient but also to capture the true cost of nursing workload. In this context, the nursing activities score (NAS) was developed as a result of modifications to the therapeutic interventions scoring system-28 (TISS-28). The NAS is a tool to measure nursing workload ICU and it has been shown to be twice as effective in measuring how nurses spend their time caring for critically ill patients than the TISS-28. This paper discuss the introduction of the NAS into everyday use in an intensive care unit in Brazil and highlights the challenges of standardisation of operational definitions, training requirements and accurate completion of the documentation when using such a tool. The rationale and steps undertaken to achieve this are outlined and the benefits of such a process are highlighted.     

Nursing workforce deployment and intensive care unit strain during the COVID-19 pandemic in Victoria,Australia

BackgroundThe COVID-19 pandemic demanded intensive care units (ICUs) globally to expand to meet increasing patient numbers requiring critical care. Critical care nurses were a finite resource in this challenge to meet growing patient numbers, necessitating redeployment of nursing staff to work in ICUs.ObjectiveOur aim was to describe the extent and manner by which the increased demand for ICU care during the COVID-19 pandemic was met by ICU nursing workforce expansion in the late 2021 and early 2022 in Victoria, Australia.MethodsThis is a retrospective cohort study of Victorian ICUs who contributed nursing data to the Critical Health Information System from 1 December 2021 to 11 April 2022. Bedside nursing workforce data, in categories as defined by Safer Care Victoria’s pandemic response guidelines, were analysed. The primary outcome was ‘insufficient ICU skill mix’—whenever a site had more patients needing 1:1 critical care nursing care than the mean daily number of experienced critical care nursing staff.ResultsOverall, data from 24 of the 47 Victorian ICUs were eligible for analysis. Insufficient ICU skill mix occurred on 10.3% (280/2725) days at 66.7% (16/24) of ICUs, most commonly during the peak phase from December to mid-February. The insufficient ICU skill mix was more likely to occur when there were more additional ICU beds open over the ‘business-as-usual’ number. Counterfactual analysis suggested that had there been no redeployment of staff to the ICU, reduced nursing ratios, with inability to provide 1:1 care, would have occurred on 15.2% (415/2725) days at 91.7% (22/24) ICUs.ConclusionThe redeployment of nurses into the ICU was necessary. However, despite this, at times, some ICUs had insufficient staff to cope with the number and acuity of patients. Further research is needed to examine the impact of ICU nursing models of care on patient outcomes and on nurse outcomes.     

Critical care nurses' perception of patient involvement in care: A qualitative focus group

Background

Patient involvement in care in the intensive care unit (ICU) is complex. Knowledge about the nature and extent of patient involvement in the Intensive care unit is scarce.

Aim

The aim of the study was to explore the critical care nursing staff's perception of patient involvement in their care in the ICU.

Study Design

A phenomenological, hermeneutic research study was carried out using qualitative data. Data were collected in two focus group interviews analysed using Ricoeur's theory of interpretation. The study was conducted in a level 2 medical-surgical 8-bed ICU in a regional hospital in Southern Denmark.

Results

Critical care nurses found it important to maintain involvement in intensive care. Depending on the patient's ability to partake in care, approaches for patient involvement ranged from (1) continually adjusting care activities according to the patient's bodily responses, (2) formation of a relationship with the patient to enable personalized care and (3) making room for self-determined care progressing with the patient's recovery.

Conclusion

Critical care nurses' perception of patient involvement depended on the patient's level of consciousness. When unconscious, patient involvement was possible but took a physical approach. However, the power inequality in the nurse–patient relationship must be expressed if patient involvement in the ICU is to take place.

Relevance to Clinical Practice

Results suggest that nurses' perception of patient involvement in the ICU depends on the patient's level of consciousness. Patient involvement may be possible even when the patient is unconscious but it takes a more physical approach. It is essential that the power inequality in the nurse–patient relationship must be expressed if patient involvement in the ICU is to take place.     

A nursing care classification system for assessing workload and determining optimal nurse staffing in a teaching hospital in China: A pre‐post intervention study

We aimed to evaluate the effectiveness of a nursing care classification system for re‐assessing nurse workload and determining staffing needs. Adequate bed–nurse ratios help manage hospital cost‐efficiency, quality of care and patient safety. A prospective pre‐post intervention study was conducted from January 2010 to December 2012 in 16 medical‐surgical units of a tertiary teaching hospital. Nursing tasks were classified into four grades of care reflecting actual workload. Units were re‐staffed accordingly and bed–nurse ratios compared with government‐authorized bed–nurse ratios. Patient satisfaction, hospital stays and mortality were evaluated pre‐ and poststaffing changes. Average bed–nurse ratio (1:0.41) exceeded the national standard (1:0.40) in 16 units, but was inadequate in five units. Re‐staffing increased average bed–nurse ratio from 1:0.41 to 1:0.48. Patients' satisfaction increased from 96.9% to 97.6%, and hospital stays decreased significantly. Nursing care classification effectively distributes nurse staffing to match patients' care levels, improving patient outcomes.     

Post-operative mortality,missed care and nurse staffing in nine countries: A cross-sectional study

BackgroundVariation in post-operative mortality rates has been associated with differences in registered nurse staffing levels. When nurse staffing levels are lower there is also a higher incidence of necessary but missed nursing care. Missed nursing care may be a significant predictor of patient mortality following surgery.AimExamine if missed nursing care mediates the observed association between nurse staffing levels and mortality.MethodData from the RN4CAST study (2009–2011) combined routinely collected data on 422,730 surgical patients from 300 general acute hospitals in 9 countries, with survey data from 26,516 registered nurses, to examine associations between nurses’ staffing, missed care and 30-day in-patient mortality. Staffing and missed care measures were derived from the nurse survey. A generalized estimation approach was used to examine the relationship between first staffing, and then missed care, on mortality. Bayesian methods were used to test for mediation.ResultsNurse staffing and missed nursing care were significantly associated with 30-day case-mix adjusted mortality. An increase in a nurse’s workload by one patient and a 10% increase in the percent of missed nursing care were associated with a 7% (OR 1.068, 95% CI 1.031–1.106) and 16% (OR 1.159 95% CI 1.039–1.294) increase in the odds of a patient dying within 30 days of admission respectively. Mediation analysis shows an association between nurse staffing and missed care and a subsequent association between missed care and mortality.ConclusionMissed nursing care, which is highly related to nurse staffing, is associated with increased odds of patients dying in hospital following common surgical procedures. The analyses support the hypothesis that missed nursing care mediates the relationship between registered nurse staffing and risk of patient mortality. Measuring missed care may provide an ‘early warning’ indicator of higher risk for poor patient outcomes.     

The relationship between workload and medical staffing levels in a paediatric cardiac intensive care unit

Objective  

To evaluate the matching between workload in a paediatric cardiac intensive care unit (ICU) and the corresponding medical staffing levels over a 24-h period.